Emerging Geo-Spatial Decision Support System for Linking Rainfall Forecast and Maize Production

Shrikant Jagtap, University of Florida, USA

Abstract

This study addressed the compatibility of an array of maize production technologies with the resource base of maize farmers in the moist savanna. The moist savanna is seen as the emerging breadbasket of sub-Saharan Africa. In the past three decades maize has spread rapidly into the moist savannas, replacing traditional cereal crops like sorghum and millet, particularly in areas with good access to fertilizer inputs and market. However, maize yields under farmers’ conditions are low, generally < 1.0 t/ha, due to various biophysical and socioeconomic constraints such as weeds, pest and diseases, erratic rainfall, erosion, low soil fertility, poor infrastructure, inappropriate policies, and post harvest crop losses. Technologies for improving maize yields have been developed in the past, but some were not adopted because they do not match with the maize production resource base of farmers. Crop management technologies such as varieties, residue management, manipulation of plant density and planting dates and N fertilization are avenues to increase yields since these can be influenced directly by farmers. Farmers and extension personnel have explored the use of a Decision Support System to take advantage of the emerging science of rainfall forecast many months before a growing season starts and promising maize production technologies to the resource base of farmers in a moist savanna to maximize resource use efficiency and food security.